Survey analysis, especially open-ended responses to questions, is a valuable data source for any organization. There are manual ways to sort and classify these data, and also options through the use of Artificial Intelligence.
ChatGPT is very popular right now, so we wanted to ask how to analyze the results of a survey when asking open-ended questions.
This was ChatGPT's conclusion:
The big question that ChatGPT needs help answering is how do we hire the people necessary to analyze hundreds of responses manually and how do medium-sized companies or customer experience teams afford the cost to access a data analyst capable of processing this qualitative data and transforming it into quantitative data.
The whole process is expensive and time-consuming, although it can be done manually. To do so, we can follow a few steps to analyze open ended questions in surveys.
1.- Categorize the responses: Read all the responses and identify common categories or patterns. For example, if many people ask about a product they want to purchase in your data, you can categorize it as a "Sales Opportunity," "Consumer Interest," etc.
2.- Calculate the frequency of the categories: Once you have read all the answers, put one or more categories (multi-label). Calculate, using excel or another program, the frequency with which each category is repeated in order to visualize this data.
3.- Analyze the relationships between categories and metadata: Cross-reference the data of the different categories with the associated metadata. For example, suppose the analyzed responses also had other data in the survey, such as NPS, CSAT, demographic analysis, etc.. In that case, you can, for example, see in which cities a category is repeated more, or by gender, NPS, etc.
4.- Analyze the relationships between categories: Analyze how the different categories are related to find patterns or detect similar categories.
5.- Present the results: The analysis needs to be able to present the results through tables, graphs, or visualizations that allow the results to be easily understood.
6.- Sentiment analysis of the responses: This is time-consuming to do manually, but it is very useful to understand if customers are happy or angry when writing a reply. This indicator allows associating some of the categories detected to the emotional state of the customers in order to maximize the impact of any decision taken on the results.
The above 6 steps can also be performed automatically using Artificial Intelligence software.
Some software that can help in this are:
- Deep-Talk.ai: Platform that fulfills the above six steps. It classifies, categorizes, sorts, visualizes, and allows cross-referencing categories and sentiment with metadata. In addition, it will enable the creation of special categories to be detected in the data and API to consume the services.
- MonkeyLearn.com: Offers several tools for text analysis. Good visualizations allow browsing the results with different graphics.
- Google AutoML: Allows users to create complex algorithms for text analysis and classification and train custom models.
Transforming qualitative data into quantitative data creates enormous value within organizations. It allows critical data sources to be incorporated into decision-making. This transformation can be done manually, but it is expensive and time-consuming; however, some tools allow automating these tasks using Artificial Intelligence.